Business intelligence (BI) refers to a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help information workers (IWs) make better business decisions. BI applications typically address activities such as decision support systems, querying, reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining. BI tools allow IWs to create and execute a certain class of BI applications over a multi-dimensional data model, such a pivot table, a cube, or other hierarchical dimensional storage, thereby achieving sophisticated analytical results from increasingly complex data.
BI applications allow information workers (IW) to collect, store, analyze, and present data and analysis results intended to inform business decisions. In a typical scenario, an IW identifies one or more data sources from which data of interest may be obtained. Information Technology (IT) personnel then apply tools and techniques of ETL (extract/transform/load) to extract the desired data from the data sources, reformat (i.e., transform) the extracted data for the IW's specific BI application, and load the transformed data into a preferred datastore. The IW can then execute a BI application (as defined by a BI document) to analyze the data of interest in the preferred datastore and present the analysis results (e.g., via visualizations). For example, an IW can collect data from a data marketplace of sports scores and statistics and execute a BI application in a spreadsheet-like tool that allows the IW to analyze the scores and statistics (such as by applying specialized calculations, adjusting data constraints, groupings, and/or filters, etc.). The BI application also defines functionality for presenting the analysis results, such as in the form of a spreadsheet-like table, a graphical chart, a user interface comparing multiple scenarios based on different input data values and analysis parameters, etc.
As mentioned above, an initial operation of BI typically involves the collection and reformatting of arbitrary complex data from various data sources into a preferred datastore and format. This collection operation is commonly referred to as “extract, transform, and load” or ETL—the data is extracted from various sources, transformed to satisfy operational needs, and loaded into the preferred datastore (e.g., a hierarchical database). ETL generally refers to bringing data, some of which is external, into the preferred datastore where subsequent BI operations can analyze it locally (e.g., at a local client or server). It should be understood, however, that some semantics may be lost when complex data is transformed into a preferred datastore format for BI. Furthermore, extraction of data from the original data sources to the local BI system may result in the loss of certain analytical capabilities provided by those original source systems.
Moreover, modern data models have shifted dramatically, introducing a new consumption and delivery model on which cloud computing is based. Cloud computing takes advantage of Internet-based, dynamically scalable, and often virtualized data resources. Such data resources can be continuously changing in both content and location. The traditional ETL model of fetching data and analyzing locally (e.g., at a single client or server) does not easily accommodate such a new data model. Furthermore, modern mobile computing devices may not be configured internally (e.g., with enough memory or a powerful enough processor) to handle the storage and computation requirements of many BI operations.